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Use of weighted visual terms and machine learning techniques for image content recognition relying on mpeg-7 visual descriptors

Published: 31 October 2008 Publication History

Abstract

We propose a technique for automatic recognition of content in images. Our technique uses machine learning methods to build classifiers which are able to decide about the presence of semantic concepts in images. Our classifiers exploit a representation of images in terms of vectors of visual terms. A visual term represents a set of visually similar regions that can be found in images. Various types of visual terms are used at the same time to take into account various similarity criteria and region representations that are available to compare regions. Specifically, we compare regions using the 5 MPEG-7 visual descriptors. An image is indexed by first using a segmentation algorithm to extract its regions, and then the image is associated with the visual terms that are more similar to the extracted regions. The proposed technique offers very good performance as demonstrated by the experiments that we performed.

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  • (2021)Knowledge formation of MPEG: Analysis using bibliographic clustering of citation networksSynthesiology10.5571/synth.2021.1_12021:1(1-17)Online publication date: 2021

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      cover image ACM Conferences
      MS '08: Proceedings of the 2nd ACM workshop on Multimedia semantics
      October 2008
      70 pages
      ISBN:9781605583167
      DOI:10.1145/1460676
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      Published: 31 October 2008

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      Author Tags

      1. image content classification
      2. machine learning

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      October 31, 2008
      British Columbia, Vancouver, Canada

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      • (2021)Knowledge formation of MPEG: Analysis using bibliographic clustering of citation networksSynthesiology10.5571/synth.2021.1_12021:1(1-17)Online publication date: 2021

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